Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction

Abstract

This investigation shows that successful forecasting models for monitoring forest health status with respect to Larch Casebearer damages can be derived using a combination of a confidence predictor framework (Conformal Prediction) in combination with a deep learning architecture (Yolo v5). A confidence predictor framework can predict the current types of diseases used to develop the model and also provide indication of new, unseen, types or degrees of disease. The user of the models is also, at the same time, provided with reliable predictions and a well-established applicability domain for the model where such reliable predictions can and cannot be expected. Furthermore, the framework gracefully handles class imbalances without explicit over- or under-sampling or category weighting which may be of crucial importance in cases of highly imbalanced datasets. The present approach also provides indication of when insufficient information has been provided as input to the model at the level of accuracy (reliability) need by the user to make subsequent decisions based on the model predictions.